Tet2 and Tet3 in B cells are required to repress CD86 and prevent autoimmunity.

A contribution of epigenetic modifications to B cell tolerance has been proposed but not directly tested. Here we report that deficiency of ten-eleven translocation (Tet) DNA demethylase family members Tet2 and Tet3 in B cells led to hyperactivation of B and T cells, autoantibody production and lupus-like disease in mice. Mechanistically, in the absence of Tet2 and Tet3, downregulation of CD86, which normally occurs following chronic exposure of self-reactive B cells to self-antigen, did not take place. The importance of dysregulated CD86 expression in Tet2- and Tet3-deficient B cells was further demonstrated by the restriction, albeit not complete, on aberrant T and B cell activation following anti-CD86 blockade. Tet2- and Tet3-deficient B cells had decreased accumulation of histone deacetylase 1 (HDAC1) and HDAC2 at the Cd86 locus. Thus, our findings suggest that Tet2- and Tet3-mediated chromatin modification participates in repression of CD86 on chronically stimulated self-reactive B cells, which contributes, at least in part, to preventing autoimmunity.

Publisher Correction: Tet2 and Tet3 in B cells are required to repress CD86 and prevent autoimmunity.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

T Follicular Helper Cell-Germinal Center B Cell Interaction Strength Regulates Entry into Plasma Cell or Recycling Germinal Center Cell Fate.

Higher- or lower-affinity germinal center (GC) B cells are directed either to plasma cell or GC recycling, respectively; however, how commitment to the plasma cell fate takes place is unclear. We found that a population of light zone (LZ) GC cells, Bcl6loCD69hi expressing a transcription factor IRF4 and higher-affinity B cell receptors (BCRs) or Bcl6hiCD69hi with lower-affinity BCRs, favored the plasma cell or recycling GC cell fate, respectively. Mechanistically, CD40 acted as a dose-dependent regulator for Bcl6loCD69hi cell formation. Furthermore, we found that expression of intercellular adhesion molecule 1 (ICAM-1) and signaling lymphocytic activation molecule (SLAM) in Bcl6loCD69hi cells was higher than in Bcl6hiCD69hi cells, thereby affording more stable T follicular helper (Tfh)-GC B cell contacts. These data support a model whereby commitment to the plasma cell begins in the GC and suggest that stability of Tfh-GC B cell contacts is key for plasma cell-prone GC cell formation.

Analyses of a Mutant Foxp3 Allele Reveal BATF as a Critical Transcription Factor in the Differentiation and Accumulation of Tissue Regulatory T Cells.

Foxp3 controls the development and function of regulatory T (Treg) cells, but it remains elusive how Foxp3 functions in vivo. Here, we established mouse models harboring three unique missense Foxp3 mutations that were identified in patients with the autoimmune disease IPEX. The I363V and R397W mutations were loss-of-function mutations, causing multi-organ inflammation by globally compromising Treg cell physiology. By contrast, the A384T mutation induced a distinctive tissue-restricted inflammation by specifically impairing the ability of Treg cells to compete with pathogenic T cells in certain non-lymphoid tissues. Mechanistically, repressed BATF expression contributed to these A384T effects. At the molecular level, the A384T mutation altered Foxp3 interactions with its specific target genes including Batf by broadening its DNA-binding specificity. Our findings identify BATF as a critical regulator of tissue Treg cells and suggest that sequence-specific perturbations of Foxp3-DNA interactions can influence specific facets of Treg cell physiology and the immunopathologies they regulate.

Three-step transcriptional priming that drives the commitment of multipotent progenitors toward B cells.

Stem cell fate is orchestrated by core transcription factors (TFs) and epigenetic modifications. Although regulatory genes that control cell type specification are identified, the transcriptional circuit and the cross-talk among regulatory factors during cell fate decisions remain poorly understood. To identify the "time-lapse" TF networks during B-lineage commitment, we used multipotent progenitors harboring a tamoxifen-inducible form of Id3, an in vitro system in which virtually all cells became B cells within 6 d by simply withdrawing 4-hydroxytamoxifen (4-OHT). Transcriptome and epigenome analysis at multiple time points revealed that ∼10%-30% of differentially expressed genes were virtually controlled by the core TFs, including E2A, EBF1, and PAX5. Strikingly, we found unexpected transcriptional priming before the onset of the key TF program. Inhibition of the immediate early genes such as Nr4a2, Klf4, and Egr1 severely impaired the generation of B cells. Integration of multiple data sets, including transcriptome, protein interactome, and epigenome profiles, identified three representative transcriptional circuits. Single-cell RNA sequencing (RNA-seq) analysis of lymphoid progenitors in bone marrow strongly supported the three-step TF network model during specification of multipotent progenitors toward B-cell lineage in vivo. Thus, our findings will provide a blueprint for studying the normal and neoplastic development of B lymphocytes.

Machine learning-based reproducible prediction of type 2 diabetes subtypes.

AIMS/HYPOTHESIS: Clustering-based subclassification of type 2 diabetes, which reflects pathophysiology and genetic predisposition, is a promising approach for providing personalised and effective therapeutic strategies. Ahlqvist's classification is currently the most vigorously validated method because of its superior ability to predict diabetes complications but it does not have strong consistency over time and requires HOMA2 indices, which are not routinely available in clinical practice and standard cohort studies. We developed a machine learning (ML) model to classify individuals with type 2 diabetes into Ahlqvist's subtypes consistently over time. METHODS: Cohort 1 dataset comprised 619 Japanese individuals with type 2 diabetes who were divided into training and test sets for ML models in a 7:3 ratio. Cohort 2 dataset, comprising 597 individuals with type 2 diabetes, was used for external validation. Participants were pre-labelled (T2Dkmeans) by unsupervised k-means clustering based on Ahlqvist's variables (age at diagnosis, BMI, HbA1c, HOMA2-B and HOMA2-IR) to four subtypes: severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), mild obesity-related diabetes (MOD) and mild age-related diabetes (MARD). We adopted 15 variables for a multiclass classification random forest (RF) algorithm to predict type 2 diabetes subtypes (T2DRF15). The proximity matrix computed by RF was visualised using a uniform manifold approximation and projection. Finally, we used a putative subset with missing insulin-related variables to test the predictive performance of the validation cohort, consistency of subtypes over time and prediction ability of diabetes complications. RESULTS: T2DRF15 demonstrated a 94% accuracy for predicting T2Dkmeans type 2 diabetes subtypes (AUCs ≥0.99 and F1 score [an indicator calculated by harmonic mean from precision and recall] ≥0.9) and retained the predictive performance in the external validation cohort (86.3%). T2DRF15 showed an accuracy of 82.9% for detecting T2Dkmeans, also in a putative subset with missing insulin-related variables, when used with an imputation algorithm. In Kaplan-Meier analysis, the diabetes clusters of T2DRF15 demonstrated distinct accumulation risks of diabetic retinopathy in SIDD and that of chronic kidney disease in SIRD during a median observation period of 11.6 (4.5-18.3) years, similarly to the subtypes using T2Dkmeans. The predictive accuracy was improved after excluding individuals with low predictive probability, who were categorised as an 'undecidable' cluster. T2DRF15, after excluding undecidable individuals, showed higher consistency (100% for SIDD, 68.6% for SIRD, 94.4% for MOD and 97.9% for MARD) than T2Dkmeans. CONCLUSIONS/INTERPRETATION: The new ML model for predicting Ahlqvist's subtypes of type 2 diabetes has great potential for application in clinical practice and cohort studies because it can classify individuals with missing HOMA2 indices and predict glycaemic control, diabetic complications and treatment outcomes with long-term consistency by using readily available variables. Future studies are needed to assess whether our approach is applicable to research and/or clinical practice in multiethnic populations.

Sodium-glucose co-transporter 2 inhibitor therapy reduces the administration frequency of anti-vascular endothelial growth factor agents in patients with diabetic macular oedema with a history of anti-vascular endothelial growth factor agent use: A cohort study using the Japanese health insurance claims database.

AIM: We assessed the effectiveness of sodium-glucose co-transporter 2 inhibitors (SGLT2is) in reducing the administration frequency of anti-vascular endothelial growth factor (VEGF) agents in patients with diabetic macular oedema (DMO) using a health insurance claims database. MATERIALS AND METHODS: This retrospective cohort study analysed health insurance claims data covering 11 million Japanese patients between 2005 and 2019. We analysed the frequency and duration of intravitreal injection of anti-VEGF agents after initiating SGLT2is or other antidiabetic drugs. RESULTS: Among 2412 matched patients with DMO, the incidence rates of anti-VEGF agent injections were 230.1 per 1000 person-year in SGLT2i users and 228.4 times per 1000 person-year in non-users, respectively, and the risk ratio for events was unchanged in both groups. Sub-analysis of each baseline characteristic of the patients showed that SGLT2is were particularly effective in patients with a history of anti-VEGF agent use [p = .027, hazard ratio (HR): 0.44, 95% confidence interval (CI): 0.22-0.91]. SGLT2is reduced the risk for the first (p = .023, HR: 0.45, 95% CI: 0.22-0.91) and second (p = .021, HR: 0.39, 95% CI: 0.17-0.89) anti-VEGF agent injections. CONCLUSIONS: There was no difference in the risk ratio for the addition of anti-VEGF therapy between the two treatment groups. However, the use of SGLT2is reduced the frequency of anti-VEGF agent administration in patients with DMO requiring anti-VEGF therapy. Therefore, SGLT2i therapy may be a novel, non-invasive, low-cost adjunctive therapy for DMO requiring anti-VEGF therapy.

Best practices for multimodal clinical data management and integration: An atopic dermatitis research case.

BACKGROUND: In clinical research on multifactorial diseases such as atopic dermatitis, data-driven medical research has become more widely used as means to clarify diverse pathological conditions and to realize precision medicine. However, modern clinical data, characterized as large-scale, multimodal, and multi-center, causes difficulties in data integration and management, which limits productivity in clinical data science. METHODS: We designed a generic data management flow to collect, cleanse, and integrate data to handle different types of data generated at multiple institutions by 10 types of clinical studies. We developed MeDIA (Medical Data Integration Assistant), a software to browse the data in an integrated manner and extract subsets for analysis. RESULTS: MeDIA integrates and visualizes data and information on research participants obtained from multiple studies. It then provides a sophisticated interface that supports data management and helps data scientists retrieve the data sets they need. Furthermore, the system promotes the use of unified terms such as identifiers or sampling dates to reduce the cost of pre-processing by data analysts. We also propose best practices in clinical data management flow, which we learned from the development and implementation of MeDIA. CONCLUSIONS: The MeDIA system solves the problem of multimodal clinical data integration, from complex text data such as medical records to big data such as omics data from a large number of patients. The system and the proposed best practices can be applied not only to allergic diseases but also to other diseases to promote data-driven medical research.

Predicting pathological highly invasive lung cancer from preoperative [18F]FDG PET/CT with multiple machine learning models.

PURPOSE: The efficacy of sublobar resection of primary lung cancer have been proven in recent years. However, sublobar resection for highly invasive lung cancer increases local recurrence. We developed and validated multiple machine learning models predicting pathological invasiveness of lung cancer based on preoperative [18F]fluorodeoxyglucose (FDG) positron emission tomography (PET) and computed tomography (CT) radiomic features. METHODS: Overall, 873 patients who underwent lobectomy or segmentectomy for primary lung cancer were enrolled. Radiomics features were extracted from preoperative PET/CT images with the PyRadiomics package. Seven machine learning models and an ensemble of all models (ENS) were evaluated after 100 iterations. In addition, the probability of highly invasive lung cancer was calculated in a nested cross-validation to assess the calibration plot and clinical usefulness and to compare to consolidation tumour ratio (CTR) on CT images, one of the generally used diagnostic criteria. RESULTS: In the training set, when PET and CT features were combined, all models achieved an area under the curve (AUC) of ≥ 0.880. In the test set, ENS showed the highest mean AUC of 0.880 and smallest standard deviation of 0.0165, and when the cutoff was 0.5, accuracy of 0.804, F1 of 0.851, precision of 0.821, and recall of 0.885. In the nested cross-validation, the AUC of 0.882 (95% CI: 0.860-0.905) showed a high discriminative ability, and the calibration plot indicated consistency with a Brier score of 0.131. A decision curve analysis showed that the ENS was valid with a threshold probability ranging from 3 to 98%. Accuracy showed an improvement of more than 8% over the CTR. CONCLUSION: The machine learning model based on preoperative [18F]FDG PET/CT images was able to predict pathological highly invasive lung cancer with high discriminative ability and stability. The calibration plot showed good consistency, suggesting its usefulness in quantitative risk assessment.

Predicting dry weight change in Hemodialysis patients using machine learning.

BACKGROUND: Machine Learning has been increasingly used in the medical field, including managing patients undergoing hemodialysis. The random forest classifier is a Machine Learning method that can generate high accuracy and interpretability in the data analysis of various diseases. We attempted to apply Machine Learning to adjust dry weight, the appropriate volume status of patients undergoing hemodialysis, which requires a complex decision-making process considering multiple indicators and the patient's physical conditions. METHODS: All medical data and 69,375 dialysis records of 314 Asian patients undergoing hemodialysis at a single dialysis center in Japan between July 2018 and April 2020 were collected from the electronic medical record system. Using the random forest classifier, we developed models to predict the probabilities of adjusting the dry weight at each dialysis session. RESULTS: The areas under the receiver-operating-characteristic curves of the models for adjusting the dry weight upward and downward were 0.70 and 0.74, respectively. The average probability of upward adjustment of the dry weight had sharp a peak around the actual change over time, while the average probability of downward adjustment of the dry weight formed a gradual peak. Feature importance analysis revealed that median blood pressure decline was a strong predictor for adjusting the dry weight upward. In contrast, elevated serum levels of C-reactive protein and hypoalbuminemia were important indicators for adjusting the dry weight downward. CONCLUSIONS: The random forest classifier should provide a helpful guide to predict the optimal changes to the dry weight with relative accuracy and may be useful in clinical practice.

Prediction of Change in Pelvic Tilt After Total Hip Arthroplasty Using Machine Learning.

BACKGROUND: A postoperative change in pelvic flexion following total hip arthroplasty (THA) is considered to be one of the causes of dislocation. This study aimed to predict the change of pelvic flexion after THA integrating preoperative and postoperative information with artificial intelligence. METHODS: This study involved 415 hips which underwent primary THA. Pelvic flexion angle (PFA) is defined as the angle created by the anterior pelvic plane and the horizontal/vertical planes in the supine/standing positions, respectively. Changes in PFA from preoperative supine position to standing position at 5 years after THA were recorded and which were defined as a 5-year change in PFA. Machine learning analysis was performed to predict 5-year change in PFA less than -20° using demographic, blood biochemical, and radiographic data as explanatory variables. Decision trees were constructed based on the important predictors for 5-year change in PFA that can be handled by humans in clinical practice. RESULTS: Among several machine learning models, random forest showed the highest accuracy (area under the curve = 0.852). Lumbo-lordotic angle, femoral anteversion angle, body mass index, pelvic tilt, and sacral slope were most important random forest predictors. By integrating these preoperative predictors with those obtained 1 year after the surgery, we developed a clinically applicable decision tree model that can predict 5-year change in PFA with area under the curve = 0.914. CONCLUSION: A machine learning model to predict 5-year change in PFA after THA has been developed by integrating preoperative and postoperative patient information, which may have capabilities for preoperative planning of THA.

Monitoring of blood biochemical markers for periprosthetic joint infection using ensemble machine learning and UMAP embedding.

INTRODUCTION: Periprosthetic joint infection (PJI) is a serious complication after total joint arthroplasty. It is important to accurately identify PJI and monitor postoperative blood biochemical marker changes for the appropriate treatment strategy. In this study, we aimed to monitor the postoperative blood biochemical characteristics of PJI by contrasting with non-PJI joint replacement cases to understand how the characteristics change postoperatively. MATERIALS AND METHODS: A total of 144 cases (52 of PJI and 92 of non-PJI) were reviewed retrospectively and split into development and validation cohorts. After exclusion of 11 cases, a total of 133 (PJI: 50, non-PJI: 83) cases were enrolled finally. An RF classifier was developed to discriminate between PJI and non-PJI cases based on 18 preoperative blood biochemical tests. We evaluated the similarity/dissimilarity between cases based on the RF model and embedded the cases in a two-dimensional space by Uniform Manifold Approximation and Projection (UMAP). The RF model developed based on preoperative data was also applied to the same 18 blood biochemical tests at 3, 6, and 12 months after surgery to analyze postoperative pathological changes in PJI and non-PJI. A Markov chain model was applied to calculate the transition probabilities between the two clusters after surgery. RESULTS: PJI and non-PJI were discriminated with the RF classifier with the area under the receiver operating characteristic curve of 0.778. C-reactive protein, total protein, and blood urea nitrogen were identified as the important factors that discriminates between PJI and non-PJI patients. Two clusters corresponding to the high- and low-risk populations of PJI were identified in the UMAP embedding. The high-risk cluster, which included a high proportion of PJI patients, was characterized by higher CRP and lower hemoglobin. The frequency of postoperative recurrence to the high-risk cluster was higher in PJI than in non-PJI. CONCLUSIONS: Although there was overlap between PJI and non-PJI, we were able to identify subgroups of PJI in the UMAP embedding. The machine-learning-based analytical approach is promising in consecutive monitoring of diseases such as PJI with a low incidence and long-term course.

Population-based Screening for Hereditary Colorectal Cancer Variants in Japan.

BACKGROUND & AIMS: Colorectal cancer (CRC) is one of the most common cancers in the world. A small proportion of CRCs can be attributed to recognizable hereditary germline variants of known CRC susceptibility genes. To better understand cancer risk, it is necessary to explore the prevalence of hereditary CRC and pathogenic variants of multiple cancer-predisposing genes in non-European populations. METHODS: We analyzed the coding regions of 27 cancer-predisposing genes in 12,503 unselected Japanese CRC patients and 23,705 controls by target sequencing and genome-wide SNP chip. Their clinical significance was assessed using ClinVar and the guidelines by ACMG/AMP. RESULTS: We identified 4,804 variants in the 27 genes and annotated them as pathogenic in 397 and benign variants in 941, of which 43.6% were novel. In total, 3.3% of the unselected CRC patients and 1.5% of the controls had a pathogenic variant. The pathogenic variants of MSH2 (odds ratio (OR) = 18.1), MLH1 (OR = 8.6), MSH6 (OR = 4.9), APC (OR = 49.4), BRIP1 (OR=3.6), BRCA1 (OR = 2.6), BRCA2 (OR = 1.9), and TP53 (OR = 1.7) were significantly associated with CRC development in the Japanese population (P-values<0.01, FDR<0.05). These pathogenic variants were significantly associated with diagnosis age and personal/family history of cancer. In total, at least 3.5% of the Japanese CRC population had a pathogenic variant or CNV of the 27 cancer-predisposing genes, indicating hereditary cancers. CONCLUSIONS: This largest study of CRC heredity in Asia can contribute to the development of guidelines for genetic testing and variant interpretation for heritable CRCs.

Exploratory analysis of plasma cytokine/chemokine levels in 6-year-old children from a birth cohort study.

This study aimed to reveal a new dimension of allergy profiles in the general population by using machine learning to explore complex relationships among various cytokines/chemokines and allergic diseases (asthma and atopic dermatitis; AD). We examined the symptoms related to asthma and AD and the plasma levels of 72 cytokines/chemokines obtained from a general population of 161 children at 6 years of age who participated in a pilot birth cohort study of the Japan Environment and Children's Study (JECS). The children whose signs and symptoms fulfilled the criteria of AD, which are mostly based on questionnaire including past symptoms, tended to have higher levels of the two chemokine ligands, CCL17 and CCL27, which are used for diagnosis of AD. On the other hand, another AD-related chemokine CCL22 level in plasma was higher only in children with visible flexural eczema, which is one of AD diagnostic criteria but was judged on the same day of blood examination unlike other criteria. Here, we also developed an innovative method of machine learning for elucidating the complex cytokine/chemokine milieu related to symptoms of allergic diseases by using clustering analysis based on the random forest dissimilarity measure that relies on artificial intelligence (AI) technique. To our surprise, the majority of children showing at least any asthma-related symptoms during the last month were divided by AI into the two clusters, either cluster-2 having elevated levels of IL-33 (related to eosinophil activation) or cluster-3 having elevated levels of CXCL7/NAP2 (related to neutrophil activation), among the total three clusters. Future studies will clarify better approach for allergic diseases by endotype classification.

Deregulated Mucosal Immune Surveillance through Gut-Associated Regulatory T Cells and PD-1+ T Cells in Human Colorectal Cancer.

Disturbed balance between immune surveillance and tolerance may lead to poor clinical outcomes in some malignancies. In paired analyses of adenocarcinoma and normal mucosa from 142 patients, we found a significant increase of the CD4/CD8 ratio and accumulation of regulatory T cells (Tregs) within the adenocarcinoma. The increased frequency of Tregs correlated with the local infiltration and extension of the tumor. There was concurrent maturation arrest, upregulation of programmed death-1 expression, and functional impairment in CD8+ T cells (CTLs) isolated from the adenocarcinoma. Adenocarcinoma-associated Tregs directly inhibit the function of normal human CTLs in vitro. With histopathological analysis, Foxp3+ Tregs were preferentially located in stroma. Concurrent transcriptome analysis of epithelial cells, stromal cells, and T cell subsets obtained from carcinomatous and normal intestinal samples from patients revealed a distinct gene expression signature in colorectal adenocarcinoma-associated Tregs, with overexpression of CCR1, CCR8, and TNFRSF9, whereas their ligands CCL4 and TNFSF9 were found upregulated in cancerous epithelium. Overexpression of WNT2 and CADM1, associated with carcinogenesis and metastasis, in cancer-associated stromal cells suggests that both cancer cells and stromal cells play important roles in the development and progression of colorectal cancer through the formation of a tumor microenvironment. The identification of CTL anergy by Tregs and the unique gene expression signature of human Tregs and stromal cells in colorectal cancer patients may facilitate the development of new therapeutics against malignancies.

Integration of genetics and miRNA-target gene network identified disease biology implicated in tissue specificity.

MicroRNAs (miRNAs) modulate the post-transcriptional regulation of target genes and are related to biology of complex human traits, but genetic landscape of miRNAs remains largely unknown. Given the strikingly tissue-specific miRNA expression profiles, we here expand a previous method to quantitatively evaluate enrichment of genome-wide association study (GWAS) signals on miRNA-target gene networks (MIGWAS) to further estimate tissue-specific enrichment. Our approach integrates tissue-specific expression profiles of miRNAs (∼1800 miRNAs in 179 cells) with GWAS to test whether polygenic signals enrich in miRNA-target gene networks and whether they fall within specific tissues. We applied MIGWAS to 49 GWASs (nTotal = 3 520 246), and successfully identified biologically relevant tissues. Further, MIGWAS could point miRNAs as candidate biomarkers of the trait. As an illustrative example, we performed differentially expressed miRNA analysis between rheumatoid arthritis (RA) patients and healthy controls (n = 63). We identified novel biomarker miRNAs (e.g. hsa-miR-762) by integrating differentially expressed miRNAs with MIGWAS results for RA, as well as novel associated loci with significant genetic risk (rs56656810 at MIR762 at 16q11; n = 91 482, P = 3.6 × 10-8). Our result highlighted that miRNA-target gene network contributes to human disease genetics in a cell type-specific manner, which could yield an efficient screening of miRNAs as promising biomarkers.

[Health and Medical Revolution Driven by Artificial Intelligence].

With the development and diversification of medical care, the importance of precision medicine, which selects a suitable treatment for the individual patient from a huge number of options, is increasing. It is often difficult to explain multifactorial diseases such as cancer and chronic inflammatory diseases by a single hypothesis. In such case, a data-driven approach is essential to construct individualized models based on comprehensive observation of the target disease. The data-driven approach utilizes artificial intelligence to extract, predict, and classify patterns of data, considering different types of variables and complex dependencies between variables. In this paper, we introduce the basic idea, typical methods, and application examples of artificial intelligence and its core technology, machine learning. We would like to discuss a new framework of medical research toward the next generation medicine, while reviewing how machine learning is used in precise prediction and data-driven redefinition of diseases.

Characterization of H7N9 influenza A viruses isolated from humans.

Avian influenza A viruses rarely infect humans; however, when human infection and subsequent human-to-human transmission occurs, worldwide outbreaks (pandemics) can result. The recent sporadic infections of humans in China with a previously unrecognized avian influenza A virus of the H7N9 subtype (A(H7N9)) have caused concern owing to the appreciable case fatality rate associated with these infections (more than 25%), potential instances of human-to-human transmission, and the lack of pre-existing immunity among humans to viruses of this subtype. Here we characterize two early human A(H7N9) isolates, A/Anhui/1/2013 (H7N9) and A/Shanghai/1/2013 (H7N9); hereafter referred to as Anhui/1 and Shanghai/1, respectively. In mice, Anhui/1 and Shanghai/1 were more pathogenic than a control avian H7N9 virus (A/duck/Gunma/466/2011 (H7N9); Dk/GM466) and a representative pandemic 2009 H1N1 virus (A/California/4/2009 (H1N1pdm09); CA04). Anhui/1, Shanghai/1 and Dk/GM466 replicated well in the nasal turbinates of ferrets. In nonhuman primates, Anhui/1 and Dk/GM466 replicated efficiently in the upper and lower respiratory tracts, whereas the replicative ability of conventional human influenza viruses is typically restricted to the upper respiratory tract of infected primates. By contrast, Anhui/1 did not replicate well in miniature pigs after intranasal inoculation. Critically, Anhui/1 transmitted through respiratory droplets in one of three pairs of ferrets. Glycan arrays showed that Anhui/1, Shanghai/1 and A/Hangzhou/1/2013 (H7N9) (a third human A(H7N9) virus tested in this assay) bind to human virus-type receptors, a property that may be critical for virus transmissibility in ferrets. Anhui/1 was found to be less sensitive in mice to neuraminidase inhibitors than a pandemic H1N1 2009 virus, although both viruses were equally susceptible to an experimental antiviral polymerase inhibitor. The robust replicative ability in mice, ferrets and nonhuman primates and the limited transmissibility in ferrets of Anhui/1 suggest that A(H7N9) viruses have pandemic potential.

Splicing variant of WDFY4 augments MDA5 signalling and the risk of clinically amyopathic dermatomyositis.

OBJECTIVES: Idiopathic inflammatory myopathies (IIMs) are a heterogeneous group of rare autoimmune diseases in which both genetic and environmental factors play important roles. To identify genetic factors of IIM including polymyositis, dermatomyositis (DM) and clinically amyopathic DM (CADM), we performed the first genome-wide association study for IIM in an Asian population. METHODS: We genotyped and tested 496 819 single nucleotide polymorphism for association using 576 patients with IIM and 6270 control subjects. We also examined the causal mechanism of disease-associated variants by in silico analyses using publicly available data sets as well as by in in vitro analyses using reporter assays and apoptosis assays. RESULTS: We identified a variant in WDFY4 that was significantly associated with CADM (rs7919656; OR=3.87; P=1.5×10-8). This variant had a cis-splicing quantitative trait locus (QTL) effect for a truncated WDFY4isoform (tr-WDFY4), with higher expression in the risk allele. Transexpression QTL analysis of this variant showed a positive correlation with the expression of NF-κB associated genes. Furthermore, we demonstrated that both WDFY4 and tr-WDFY4 interacted with pattern recognition receptors such as TLR3, TLR4, TLR9 and MDA5 and augmented the NF-κB activation by these receptors. WDFY4 isoforms also enhanced MDA5-induced apoptosis to a greater extent in the tr-WDFY4-transfected cells. CONCLUSIONS: As CADM is characterised by the appearance of anti-MDA5 autoantibodies and severe lung inflammation, the WDFY4 variant may play a critical role in the pathogenesis of CADM.

Experimental adaptation of an influenza H5 HA confers respiratory droplet transmission to a reassortant H5 HA/H1N1 virus in ferrets.

Highly pathogenic avian H5N1 influenza A viruses occasionally infect humans, but currently do not transmit efficiently among humans. The viral haemagglutinin (HA) protein is a known host-range determinant as it mediates virus binding to host-specific cellular receptors. Here we assess the molecular changes in HA that would allow a virus possessing subtype H5 HA to be transmissible among mammals. We identified a reassortant H5 HA/H1N1 virus-comprising H5 HA (from an H5N1 virus) with four mutations and the remaining seven gene segments from a 2009 pandemic H1N1 virus-that was capable of droplet transmission in a ferret model. The transmissible H5 reassortant virus preferentially recognized human-type receptors, replicated efficiently in ferrets, caused lung lesions and weight loss, but was not highly pathogenic and did not cause mortality. These results indicate that H5 HA can convert to an HA that supports efficient viral transmission in mammals; however, we do not know whether the four mutations in the H5 HA identified here would render a wholly avian H5N1 virus transmissible. The genetic origin of the remaining seven viral gene segments may also critically contribute to transmissibility in mammals. Nevertheless, as H5N1 viruses continue to evolve and infect humans, receptor-binding variants of H5N1 viruses with pandemic potential, including avian-human reassortant viruses as tested here, may emerge. Our findings emphasize the need to prepare for potential pandemics caused by influenza viruses possessing H5 HA, and will help individuals conducting surveillance in regions with circulating H5N1 viruses to recognize key residues that predict the pandemic potential of isolates, which will inform the development, production and distribution of effective countermeasures.